Working with proteomics data in R

Proteomics in Biomedicine

Gorka Prieto <>

University of the Basque Country (UPV/EHU)

September 29, 2025

First of all …

  • Click the following link to start downloading a file (15 minutes) we will need:


Virtual machine with all the required software and datasets (*.ova)

  • Already downloaded at C:\Usuarios\Acceso Publico\Documentos Publicos

1 Goal

  • Understand the steps and data structures of a shotgun proteomics bioinformatics workflow:
    • After the search engine (already covered in previous sessions)
    • In a practical way with a computer
    • Using free/open-source solutions instead of “black box” software:
      • R notebooks

2 R notebooks

2.1 What’s R? Why to use it?

  • R is a programming language designed for statistical computing and graphics
  • Free software and open-source (GPL license)
  • Thousands of bioinformatic packages available (see Bioconductor repository) with active contributions and support
  • You have control to fine-tune or adapt the workflow to your needs, no longer a “black box” (more didactic, therefore)
  • Notebooks combine text, code and its execution result (e.g. figures) in a very convenient way (e.g. this presentation)

Dont’t worry!

  • This is not a programming course
  • You will not have to code in this lesson (hopefully on your own)
  • I will already provide the R notebooks
  • Just understand the steps, make little changes and run the notebook

2.2 R notebook example

---
title: "My first notebook"
output: html_notebook
---

# First section

The title above will be rendered with a bigger font and this text with normal font.

## Subsection

The following block is a code chunk in R that will be executed (when pressing
Ctrl+Shift+Enter) and its output included in this same document:

` ` `{r}
x <- 2
x+1
` ` ` 
3

2.3 R syntax

  • We can make mathematical operations as usual: 3*4 + 2^3
  • Often we want to save the result into a variable: x <- 5/4 or 5/4 -> x
  • And use that variable later: x * 3
  • We can also use predefined functions: min(3, 5, 2)
  • And pass parameters to them: min(3, NA, 5, 2, na.rm = TRUE)
  • We can import third-party functions: library(tidyverse)
  • We can operate with different data types:
    • numeric: 3.14
    • character: "E2F1" or 'E2F1'
    • vector: c(4, 6, 9)
    • logical: TRUE, FALSE
    • Not Available: NA
    • matrix, dataframe, tibble: data in rows and columns (with names)
    • etc.

2.4 Tidyverse

  • Will work with data (PSMs, peptides, proteins, etc.) organized in tibbles (≈dataframes):
    • Like a spreadsheet, with rows (observations) and columns (variables)
  • Tidyverse provides useful packages for data science:

Example

  • Tibble with target and decoy columns and 5 rows:
example <- tibble(
  decoy = c(0, 0, 1, 1, 2),
  target = seq(10, 50, by=10)
)
example 
# A tibble: 5 × 2
  decoy target
  <dbl>  <dbl>
1     0     10
2     0     20
3     1     30
4     1     40
5     2     50
  • Compute a new tibble with an additional FDR=decoy/target column:
example %>% 
  mutate(FDR = decoy/target)
# A tibble: 5 × 3
  decoy target    FDR
  <dbl>  <dbl>  <dbl>
1     0     10 0     
2     0     20 0     
3     1     30 0.0333
4     1     40 0.025 
5     2     50 0.04

3 Working environment

3.1 Virtual machine

  1. Launch VirtualBox and import (~2 minutes) the downloaded file (*.ova)

  1. Configure the virtual machine hardware according to your available resources

  2. Start the virtual machine and log-in using your LDAP credentials

3.2 R Studio

  • Click on Projects/R/ProteomicsBiomedicine/R.Rproj to open the R Studio project provided